Factors Associated with Head Injury from Road Traffic Accidents Among Motorcycle Travelers in State Hospitals, Amnatcharoen Province, 2022-2023
Keywords:
Factors , Head Injury , Motorcycle travelersAbstract
This study aimed to examine the factors associated with head injuries among victims of motorcycle-related road traffic accidents who received treatment at emergency room in public hospitals in Amnat Charoen Province during 2022–2023. A cross-sectional analytical study design was employed, using secondary data from the Injury Surveillance (IS) system. From a total of 8,597 reported road traffic injury cases, 4,236 cases were selected based on defined inclusion and exclusion criteria. The independent variables included age, type of travelers, alcohol consumption, helmet use, road type, injury mechanism, referral and death. The dependent variable was head injury. Descriptive statistics were used to summarize frequency, percentage, mean, and standard deviation. Inferential statistics included binary and multiple logistic regression. Odds Ratios (OR), Adjusted Odds Ratios (ORAdj), and 95% confidence intervals (95% CI) were reported, with a significance level set at p = 0.05. The majority of injured individuals were male (59.11%) with an average age of 32 years (S.D. = 18.71), and most were motorcycle riders (84.32%). Alcohol consumption was reported in 17.36% of cases, while 90.44% were not wearing helmets. Most accidents occurred on main roads (76.77%), with the predominant mechanisms being falls, overturns, or submersion (56.02%). EMS referral was not used in 51.65% of cases, and 1.27% resulted in fatalities. Statistically significant factors associated with head injuries included alcohol consumption (ORAdj = 1.79; 95% CI = 1.43–2.23), not wearing a helmet (ORAdj = 1.72; 95% CI = 1.23–2.40), accidents on minor roads (ORAdj = 1.91; 95% CI = 1.55–2.34), EMS referral (ORAdj = 1.73; 95% CI = 1.44–2.07), and death (ORAdj = 34.97; 95% CI = 4.55–268.73). Age, type of travelers, and injury mechanism were not significantly associated with head injuries. The findings provide evidence for developing targeted prevention strategies aligned with the local context. Recommendations include identifying and improving high-risk locations on minor roads, enhancing digital risk communication outreach, applying artificial intelligence (AI) technologies for rider screening, expanding referral coverage, and strengthening EMS staff capacity. Additionally, these results offer a foundation for future operational research aimed at designing and evaluating head injury prevention interventions at the community and policy levels.
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